scholarly journals OPTIMIZATION OF TRUSSES WITH SELF-ADAPTIVE APPROACH IN GENETIC ALGORITHMS

2016 ◽  
Vol 9 (4) ◽  
pp. 67-78 ◽  
Author(s):  
Krzysztof GRYGIEREK
Keyword(s):  
Genetic Algorithms ◽  
Adaptive Approach ◽  
Self Adaptive

An efficient and self-adaptive approach forQvalue optimization

2016 ◽  
Vol 65 (1) ◽  
pp. 280-291 ◽  
Author(s):  
Gulan Zhang ◽  
Ximing Wang ◽  
Zhenhua He
Keyword(s):  
Adaptive Approach ◽  
Self Adaptive

scholarly journals A Novel Mating Approach for Genetic Algorithms

2013 ◽  
Vol 21 (2) ◽  
pp. 197-229 ◽  
Author(s):  
Severino F. Galán ◽  
Ole J. Mengshoel ◽  
Rafael Pinter
Keyword(s):  
Genetic Algorithms ◽  
Random Mating ◽  
Adaptive Strategy ◽  
Mating Strategies ◽  
Function Optimization ◽  
The Novel ◽  
Independent Manner ◽  
Novel Approach ◽  
Domain Independent ◽  
Self Adaptive

Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called the mating index that allows different mating strategies to be developed within a uniform framework: an exploitative strategy called best-first, an explorative strategy called best-last, and an adaptive strategy called self-adaptive. Self-adaptive mating is defined in the context of the novel algorithm, and aims to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, increasing the mating index improves performance. In the case of the self-adaptive mating strategy, the experiments give strong results for several case studies.

Optimization of 3d trusses with adaptive approach in genetic algorithms

2006 ◽  
Vol 28 (7) ◽  
pp. 1019-1027 ◽  
Author(s):  
Vedat Toğan ◽  
Ayşe T. Daloğlu
Keyword(s):  
Genetic Algorithms ◽  
Adaptive Approach
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